The complexity of ecological communities creates challenges to understanding multi-host parasite transmission. Pronounced heterogeneity in transmission among individuals, species and across space is the rule rather than the exception. Community ecologists are beginning to make great strides in predicting multi-species interactions using a trait-based rather than taxonomic approach, identifying key functional attributes of organisms and environments that are important to understanding the system. At the same time, disease ecologists generally use network modeling to understand parasite transmission in complex communities. Yet the merging of a trait-based approach with network modeling to understand multi-host transmission across space and time is in its infancy. We will take advantage of a highly tractable system - diverse communities of bees that transmit parasites via networks of flowering plants - to merge trait-based theory with network modeling, introducing a novel theoretical framework for multi-host parasite transmission in complex communities. We will collect empirical contact pattern and trait data from plant-pollinator networks to identify aspects of network structure that contribute to disease spread. Through the collection of extensive data on bee traits, floral traits and parasite spread, we will use machine learning techniques to construct and parameterize trait-based models of disease transmission in order to make falsifiable predictions for further testing. We will then test model predictions via whole-community manipulations of bees, parasites and plants in mesocosms. Such whole-community manipulations will offer unparalleled insight into the specific network patterns and traits that shape transmission in multi-host communities.